Computer tomographic imaging technologies facilitate analysis of volumetric anatomical structures by providing a series of cross-sectional slices that collectively define a structure. Using any of various techniques, such as ultrasound, positron emission, X-rays (in the case of CT scans) or nuclear magnetic resonance (in the case of MRI scans), a sensor array scans through the structure at closely spaced intervals; the nature of the information appearing in each cross-sectional slice depends on the imaging modality and its interactions with the various physiological and non-physiological components of the structure and its surroundings.
A single scan, however, may not suffice for clinical diagnosis, therapy planning, detection of anatomical or functional changes, or outcome evaluation. For these purposes it is frequently necessary to conduct a plurality of scans to permit comparison of results. However, in order to relate sets of tomographic images to one another, the individual slices must be superposable with considerable precision. Ordinarily, and despite the best efforts of technicians, movement of the sensor array, the patient, or even the anatomical structure under study shift the positions of imaged subject matter in one scan relative to another scan. The use of different imaging modalities or different imaging protocols within one modality can complicate matters still further, since fluctuations in equipment response and clinical-anatomical changes in the examined subject typically alter the contrast and shape of imaged components. Indeed, this is frequently the very reason for utilizing multiple modalities: pooling the special capabilities of different imagers into a common space provides a combined image containing more information than could be generated using any particular modality in isolation. For example, the high-quality bone information obtained with a CT scan can be combined with the high-quality tissue information obtained with an MRI scan of the same anatomy.
Accordingly, numerous techniques have been employed to bring the various image slices of different scans of the same subject into registration, a process known as "intrasubject alignment." The result of the process is a transformation that best maps corresponding features from one image set to another, so that corresponding features of different images occupy equivalent spatial locations. The prior art includes rigid-body techniques; piecewise linear and nonlinear algorithms; the matching of principal axes, homologous points, surface contours, homologous surfaces, or corresponding high-curvature lines on surfaces; and three-dimensional image correlation.
These techniques can be categorized somewhat loosely as either correlation methods that utilize pixel values directly; algorithms that utilize low-level features such as edges; and algorithms that utilize high-level features that are recognized as subelements of the imagery. Most of these techniques are "feature-based" in the sense that they require either the operator or the algorithm to identify corresponding anatomic structures in both the original and target image stacks, or, alternatively, to remove structures (such as the scalp and skull meninges). Either requirement introduces an additional source of error; in particular, operator error can be severe on occasion and is reduced only through elaborate training in the the technique and the relevant anatomy. Pixel-based algorithms, on the other hand, can exhibit sensitivity to missing data. More generally, although the computational speed of available imaging workstations is high and increasing, the alignment procedures for whole-volume (three-dimensional) sampling still require considerable computational capacity and prolonged execution times.